mudiff.mblacc(SampleSizeMeans)
mudiff.mblacc()所属R语言包：SampleSizeMeans
Bayesian sample size determination for differences in normal means using the mixed Bayesian/likelihood Average Coverage Criterion
贝叶斯样本量确定为正常手段使用的混合贝叶斯/可能性平均覆盖率标准的差异
译者：生物统计家园网 机器人LoveR
描述----------Description----------
The function mudiff.mblacc returns the required sample sizes
函数mudiff.mblacc返回所需的样本量
用法----------Usage----------
mudiff.mblacc(len, alpha1, beta1, alpha2, beta2, level = 0.95, m = 10000, mcs = 3)
参数----------Arguments----------
参数：len
The desired fixed length of the posterior credible interval for the difference between the two unknown means
后置信区间所需的固定长度的两个未知装置之间的差异
参数：alpha1
First prior parameter of the Gamma density for the precision (reciprocal of the variance) for the first population
第一现有的Gamma密度参数的精度（方差的倒数）为第一人口
参数：beta1
Second prior parameter of the Gamma density for the precision (reciprocal of the variance) for the first population
第二现有的Gamma密度参数的精度（方差的倒数）为第一人口
参数：alpha2
First prior parameter of the Gamma density for the precision (reciprocal of the variance) for the second population
第一现有的Gamma密度参数的精度（方差的倒数）为所述第二人口
参数：beta2
Second prior parameter of the Gamma density for the precision (reciprocal of the variance) for the second population
第二现有的Gamma密度参数的精度（方差的倒数）为所述第二人口
参数：level
The desired average coverage probability of the posterior credible interval (e.g., 0.95)
所需的平均后的可信区间（例如，0.95）的覆盖概率
参数：m
The number of points simulated from the preposterior distribution of the data. For each point, the probability coverage of the highest posterior density interval of fixed length len is estimated, in order to approximate the average coverage probability. Usually 10000 is sufficient, but one can increase this number at the expense of program running time.
点模拟从的preposterior的分布的数据的数量。对于每一个点，估计最高后验概率密度间隔固定长度len的概率覆盖，以近似的平均覆盖概率。通常为10000足够了，但在程序运行时间为代价的，可以增加这个数字。
参数：mcs
The Maximum number of Consecutive Steps allowed in the same direction in the march towards the optimal sample size, before the result for the next upper/lower bound is cross-checked. In our experience, mcs = 3 is a good choice.
允许在同一方向的连续步骤的最佳样本量，在迈向下一个上/下限的结果是交叉检查的最大数量。根据我们的经验，MCS = 3是一个不错的选择。
Details
详细信息----------Details----------
Assume that a sample from each of two populations will be collected in order to estimate the difference between two independent normal means. Assume that the precision within each of the two the populations are unknown, but have prior information in the form of Gamma(alpha1, beta1) and Gamma(alpha2, beta2) densities, respectively. The function mudiff.mblacc returns the required sample sizes to attain the desired average coverage probability level for the posterior credible interval of fixed length len for the difference between the two unknown means.<br><br> This function uses a Mixed Bayesian/Likelihood (MBL) approach. MBL approaches use the prior information to derive the predictive distribution of the data, but use only the likelihood function for final inferences.
假设以估计之间的差，两个独立的正常手段，将被收集在一个样品从每两个群体。假设的精度内的两个人口是未知的，但有先验信息的形式伽玛（α1，β1）和γ（ALPHA2，β2）的密度，分别。函数mudiff.mblacc返回所需的样本量，以达到所需的平均覆盖概率水平为固定长度len后的置信区间的两个未知的手段之间的差异。<BR> <BR>该函数使用一个混合的贝叶斯/似然法（MBL）的方法。 MBL方法使用的先验信息，得到的预测分布的数据，但只使用似然函数为最终推断。
值----------Value----------
The required sample sizes (n1, n2) for each group given the inputs to the function.
各组所需的样本量（N1，N2）输入的功能。
注意----------Note----------
The sample sizes are calculated via Monte Carlo simulations, and therefore may vary from one call to the next.
通过Monte Carlo模拟计算样本量，因此可能会有所不同从一个调用到下一个。
（作者）----------Author(s)----------
Lawrence Joseph <a href="mailto:lawrence.joseph@mcgill.ca">lawrence.joseph@mcgill.ca</a> and Patrick Belisle
参考文献----------References----------
Bayesian sample size determination for Normal means and differences between Normal means<br>
参见----------See Also----------
mudiff.mblalc, mudiff.mblmodwoc, mudiff.mblacc.equalvar, mudiff.mblalc.equalvar, mudiff.mblmodwoc.equalvar, mudiff.mbl.varknown, mudiff.acc, mudiff.alc, mudiff.modwoc, mudiff.acc.equalvar, mudiff.alc.equalvar, mudiff.modwoc.equalvar, mudiff.varknown, mudiff.freq, mu.mblacc, mu.mblalc, mu.mblmodwoc, mu.mbl.varknown, mu.acc, mu.alc, mu.modwoc, mu.varknown, mu.freq
mudiff.mblalc，mudiff.mblmodwoc，mudiff.mblacc.equalvar，mudiff.mblalc.equalvar，mudiff.mblmodwoc.equalvar，mudiff.mbl.varknown，mudiff.acc，mudiff.alc，mudiff.modwoc，mudiff.acc.equalvar，mudiff.alc.equalvar，mudiff.modwoc.equalvar，mudiff.varknown，mudiff.freq，mu.mblacc，mu.mblalc，mu.mblmodwoc ，mu.mbl.varknown，mu.acc，mu.alc，mu.modwoc，mu.varknown，mu.freq
实例----------Examples----------
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
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发表于 2012-9-29 21:39:50
